Overview

Dataset statistics

Number of variables15
Number of observations940
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory164.4 KiB
Average record size in memory179.1 B

Variable types

Numeric14
Categorical1

Alerts

TotalSteps is highly overall correlated with TotalDistance and 8 other fieldsHigh correlation
TotalDistance is highly overall correlated with TotalSteps and 8 other fieldsHigh correlation
TrackerDistance is highly overall correlated with TotalSteps and 8 other fieldsHigh correlation
VeryActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly overall correlated with TotalSteps and 6 other fieldsHigh correlation
FairlyActiveMinutes is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
ActivityDate is uniformly distributedUniform
TotalSteps has 77 (8.2%) zerosZeros
TotalDistance has 78 (8.3%) zerosZeros
TrackerDistance has 78 (8.3%) zerosZeros
LoggedActivitiesDistance has 908 (96.6%) zerosZeros
VeryActiveDistance has 413 (43.9%) zerosZeros
ModeratelyActiveDistance has 386 (41.1%) zerosZeros
LightActiveDistance has 85 (9.0%) zerosZeros
SedentaryActiveDistance has 858 (91.3%) zerosZeros
VeryActiveMinutes has 409 (43.5%) zerosZeros
FairlyActiveMinutes has 384 (40.9%) zerosZeros
LightlyActiveMinutes has 84 (8.9%) zerosZeros

Reproduction

Analysis started2024-03-21 14:29:15.827903
Analysis finished2024-03-21 14:29:50.591590
Duration34.76 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct33
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8554074 × 109
Minimum1.5039604 × 109
Maximum8.8776894 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:50.738634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.5039604 × 109
5-th percentile1.6245801 × 109
Q12.320127 × 109
median4.445115 × 109
Q36.9621811 × 109
95-th percentile8.7920097 × 109
Maximum8.8776894 × 109
Range7.373729 × 109
Interquartile range (IQR)4.6420541 × 109

Descriptive statistics

Standard deviation2.4248055 × 109
Coefficient of variation (CV)0.4994031
Kurtosis-1.2730307
Mean4.8554074 × 109
Median Absolute Deviation (MAD)2.418763 × 109
Skewness0.1771249
Sum4.5640829 × 1012
Variance5.8796816 × 1018
MonotonicityIncreasing
2024-03-21T19:59:50.894896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1503960366 31
 
3.3%
4319703577 31
 
3.3%
8583815059 31
 
3.3%
8378563200 31
 
3.3%
8053475328 31
 
3.3%
7086361926 31
 
3.3%
6962181067 31
 
3.3%
5553957443 31
 
3.3%
4702921684 31
 
3.3%
4558609924 31
 
3.3%
Other values (23) 630
67.0%
ValueCountFrequency (%)
1503960366 31
3.3%
1624580081 31
3.3%
1644430081 30
3.2%
1844505072 31
3.3%
1927972279 31
3.3%
2022484408 31
3.3%
2026352035 31
3.3%
2320127002 31
3.3%
2347167796 18
1.9%
2873212765 31
3.3%
ValueCountFrequency (%)
8877689391 31
3.3%
8792009665 29
3.1%
8583815059 31
3.3%
8378563200 31
3.3%
8253242879 19
2.0%
8053475328 31
3.3%
7086361926 31
3.3%
7007744171 26
2.8%
6962181067 31
3.3%
6775888955 26
2.8%

ActivityDate
Categorical

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size61.6 KiB
12-04-2016
 
33
14-04-2016
 
33
15-04-2016
 
33
13-04-2016
 
33
23-04-2016
 
32
Other values (26)
776 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters9400
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12-04-2016
2nd row13-04-2016
3rd row14-04-2016
4th row15-04-2016
5th row16-04-2016

Common Values

ValueCountFrequency (%)
12-04-2016 33
 
3.5%
14-04-2016 33
 
3.5%
15-04-2016 33
 
3.5%
13-04-2016 33
 
3.5%
23-04-2016 32
 
3.4%
29-04-2016 32
 
3.4%
28-04-2016 32
 
3.4%
26-04-2016 32
 
3.4%
25-04-2016 32
 
3.4%
24-04-2016 32
 
3.4%
Other values (21) 616
65.5%

Length

2024-03-21T19:59:51.051188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12-04-2016 33
 
3.5%
15-04-2016 33
 
3.5%
13-04-2016 33
 
3.5%
14-04-2016 33
 
3.5%
22-04-2016 32
 
3.4%
21-04-2016 32
 
3.4%
16-04-2016 32
 
3.4%
18-04-2016 32
 
3.4%
19-04-2016 32
 
3.4%
20-04-2016 32
 
3.4%
Other values (21) 616
65.5%

Most occurring characters

ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
9 91
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7520
80.0%
Dash Punctuation 1880
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2227
29.6%
2 1375
18.3%
1 1357
18.0%
6 1033
13.7%
4 705
 
9.4%
5 423
 
5.6%
3 125
 
1.7%
7 93
 
1.2%
9 91
 
1.2%
8 91
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 1880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9400
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
9 91
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2227
23.7%
- 1880
20.0%
2 1375
14.6%
1 1357
14.4%
6 1033
11.0%
4 705
 
7.5%
5 423
 
4.5%
3 125
 
1.3%
7 93
 
1.0%
9 91
 
1.0%

TotalSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct842
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7637.9106
Minimum0
Maximum36019
Zeros77
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:51.199510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13789.75
median7405.5
Q310727
95-th percentile15485.1
Maximum36019
Range36019
Interquartile range (IQR)6937.25

Descriptive statistics

Standard deviation5087.1507
Coefficient of variation (CV)0.66603957
Kurtosis1.1691112
Mean7637.9106
Median Absolute Deviation (MAD)3446.5
Skewness0.65289494
Sum7179636
Variance25879103
MonotonicityNot monotonic
2024-03-21T19:59:51.371346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
8.2%
244 2
 
0.2%
6708 2
 
0.2%
9167 2
 
0.2%
6175 2
 
0.2%
10538 2
 
0.2%
1510 2
 
0.2%
8538 2
 
0.2%
7937 2
 
0.2%
4363 2
 
0.2%
Other values (832) 845
89.9%
ValueCountFrequency (%)
0 77
8.2%
4 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
29 1
 
0.1%
31 1
 
0.1%
42 1
 
0.1%
44 1
 
0.1%
ValueCountFrequency (%)
36019 1
0.1%
29326 1
0.1%
27745 1
0.1%
23629 1
0.1%
23186 1
0.1%
22988 1
0.1%
22770 1
0.1%
22359 1
0.1%
22244 1
0.1%
22026 1
0.1%

TotalDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct615
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4897021
Minimum0
Maximum28.03
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:51.558931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.62
median5.245
Q37.7125
95-th percentile11.6565
Maximum28.03
Range28.03
Interquartile range (IQR)5.0925

Descriptive statistics

Standard deviation3.9246059
Coefficient of variation (CV)0.71490325
Kurtosis3.1130182
Mean5.4897021
Median Absolute Deviation (MAD)2.56
Skewness1.1262736
Sum5160.32
Variance15.402532
MonotonicityNot monotonic
2024-03-21T19:59:51.733552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.6 5
 
0.5%
0.01 5
 
0.5%
3.91 4
 
0.4%
4.95 4
 
0.4%
1.79 4
 
0.4%
4.33 4
 
0.4%
2.68 4
 
0.4%
3.51 4
 
0.4%
4.9 4
 
0.4%
Other values (605) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.01 5
 
0.5%
0.02 1
 
0.1%
0.03 2
 
0.2%
0.04 1
 
0.1%
0.08 1
 
0.1%
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.13 1
 
0.1%
ValueCountFrequency (%)
28.03 1
0.1%
26.72 1
0.1%
25.29 1
0.1%
20.65 1
0.1%
20.4 1
0.1%
19.56 1
0.1%
19.34 1
0.1%
18.98 1
0.1%
18.25 1
0.1%
18.11 1
0.1%

TrackerDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct613
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4753511
Minimum0
Maximum28.03
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:51.905436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.62
median5.245
Q37.71
95-th percentile11.6565
Maximum28.03
Range28.03
Interquartile range (IQR)5.09

Descriptive statistics

Standard deviation3.9072759
Coefficient of variation (CV)0.71361195
Kurtosis3.203889
Mean5.4753511
Median Absolute Deviation (MAD)2.555
Skewness1.1345496
Sum5146.83
Variance15.266805
MonotonicityNot monotonic
2024-03-21T19:59:52.061685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.6 5
 
0.5%
0.01 5
 
0.5%
3.91 4
 
0.4%
2.68 4
 
0.4%
1.79 4
 
0.4%
4.33 4
 
0.4%
4.95 4
 
0.4%
3.51 4
 
0.4%
8.74 4
 
0.4%
Other values (603) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.01 5
 
0.5%
0.02 1
 
0.1%
0.03 2
 
0.2%
0.04 1
 
0.1%
0.08 1
 
0.1%
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.13 1
 
0.1%
ValueCountFrequency (%)
28.03 1
0.1%
26.72 1
0.1%
25.29 1
0.1%
20.65 1
0.1%
20.4 1
0.1%
19.56 1
0.1%
19.34 1
0.1%
18.98 1
0.1%
18.25 1
0.1%
18.11 1
0.1%

LoggedActivitiesDistance
Real number (ℝ)

Distinct18
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10812766
Minimum0
Maximum4.94
Zeros908
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:52.217146image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.94
Range4.94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61972451
Coefficient of variation (CV)5.7314152
Kurtosis41.315519
Mean0.10812766
Median Absolute Deviation (MAD)0
Skewness6.2989054
Sum101.64
Variance0.38405847
MonotonicityNot monotonic
2024-03-21T19:59:52.357766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 908
96.6%
2.09 9
 
1.0%
2.25 7
 
0.7%
4.91 2
 
0.2%
4.08 1
 
0.1%
2.79 1
 
0.1%
3.17 1
 
0.1%
4.87 1
 
0.1%
4.85 1
 
0.1%
3.29 1
 
0.1%
Other values (8) 8
 
0.9%
ValueCountFrequency (%)
0 908
96.6%
1.96 1
 
0.1%
2.09 9
 
1.0%
2.25 7
 
0.7%
2.79 1
 
0.1%
2.83 1
 
0.1%
3.17 1
 
0.1%
3.29 1
 
0.1%
4.08 1
 
0.1%
4.85 1
 
0.1%
ValueCountFrequency (%)
4.94 1
0.1%
4.93 1
0.1%
4.92 1
0.1%
4.91 2
0.2%
4.89 1
0.1%
4.88 1
0.1%
4.87 1
0.1%
4.86 1
0.1%
4.85 1
0.1%
4.08 1
0.1%

VeryActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct333
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5026809
Minimum0
Maximum21.92
Zeros413
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:52.514017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.21
Q32.0525
95-th percentile6.403
Maximum21.92
Range21.92
Interquartile range (IQR)2.0525

Descriptive statistics

Standard deviation2.6589412
Coefficient of variation (CV)1.769465
Kurtosis11.910951
Mean1.5026809
Median Absolute Deviation (MAD)0.21
Skewness2.99617
Sum1412.52
Variance7.0699681
MonotonicityNot monotonic
2024-03-21T19:59:52.699593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
43.9%
0.07 9
 
1.0%
0.06 6
 
0.6%
0.14 5
 
0.5%
0.33 5
 
0.5%
0.34 4
 
0.4%
1.06 4
 
0.4%
0.36 4
 
0.4%
1.01 4
 
0.4%
2.79 4
 
0.4%
Other values (323) 482
51.3%
ValueCountFrequency (%)
0 413
43.9%
0.02 2
 
0.2%
0.04 1
 
0.1%
0.05 3
 
0.3%
0.06 6
 
0.6%
0.07 9
 
1.0%
0.08 4
 
0.4%
0.09 1
 
0.1%
0.11 3
 
0.3%
0.12 3
 
0.3%
ValueCountFrequency (%)
21.92 1
0.1%
21.66 1
0.1%
13.4 1
0.1%
13.26 1
0.1%
13.24 1
0.1%
13.22 1
0.1%
13.13 1
0.1%
13.07 1
0.1%
12.79 1
0.1%
12.54 1
0.1%

ModeratelyActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56754255
Minimum0
Maximum6.48
Zeros386
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:52.858799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.24
Q30.8
95-th percentile2.13
Maximum6.48
Range6.48
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.88358032
Coefficient of variation (CV)1.556853
Kurtosis10.125629
Mean0.56754255
Median Absolute Deviation (MAD)0.24
Skewness2.7711936
Sum533.49
Variance0.78071419
MonotonicityNot monotonic
2024-03-21T19:59:53.030671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
41.1%
0.2 9
 
1.0%
0.28 9
 
1.0%
0.4 9
 
1.0%
0.25 8
 
0.9%
0.31 8
 
0.9%
0.93 8
 
0.9%
0.42 8
 
0.9%
0.27 7
 
0.7%
0.57 7
 
0.7%
Other values (201) 481
51.2%
ValueCountFrequency (%)
0 386
41.1%
0.01 1
 
0.1%
0.02 1
 
0.1%
0.03 3
 
0.3%
0.04 3
 
0.3%
0.05 3
 
0.3%
0.06 3
 
0.3%
0.07 2
 
0.2%
0.08 4
 
0.4%
0.09 2
 
0.2%
ValueCountFrequency (%)
6.48 1
 
0.1%
6.21 1
 
0.1%
5.6 1
 
0.1%
5.4 1
 
0.1%
5.24 1
 
0.1%
5.12 1
 
0.1%
4.58 1
 
0.1%
4.56 1
 
0.1%
4.35 1
 
0.1%
4.22 3
0.3%

LightActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct491
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3408191
Minimum0
Maximum10.71
Zeros85
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:53.224635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.945
median3.365
Q34.7825
95-th percentile6.462
Maximum10.71
Range10.71
Interquartile range (IQR)2.8375

Descriptive statistics

Standard deviation2.0406554
Coefficient of variation (CV)0.61082486
Kurtosis-0.18030027
Mean3.3408191
Median Absolute Deviation (MAD)1.42
Skewness0.18224747
Sum3140.37
Variance4.1642744
MonotonicityNot monotonic
2024-03-21T19:59:53.400820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
9.0%
4.18 6
 
0.6%
3.17 6
 
0.6%
4.88 6
 
0.6%
3.23 6
 
0.6%
3.94 5
 
0.5%
3.26 5
 
0.5%
0.01 5
 
0.5%
4.46 5
 
0.5%
5.41 5
 
0.5%
Other values (481) 806
85.7%
ValueCountFrequency (%)
0 85
9.0%
0.01 5
 
0.5%
0.02 1
 
0.1%
0.03 3
 
0.3%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.13 2
 
0.2%
ValueCountFrequency (%)
10.71 1
0.1%
10.57 1
0.1%
10.3 1
0.1%
9.48 1
0.1%
9.46 1
0.1%
8.97 1
0.1%
8.79 1
0.1%
8.68 1
0.1%
8.41 1
0.1%
8.27 1
0.1%

SedentaryActiveDistance
Real number (ℝ)

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001606383
Minimum0
Maximum0.11
Zeros858
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:53.541821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.01
Maximum0.11
Range0.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0073461763
Coefficient of variation (CV)4.5731164
Kurtosis99.127444
Mean0.001606383
Median Absolute Deviation (MAD)0
Skewness8.589899
Sum1.51
Variance5.3966306 × 10-5
MonotonicityNot monotonic
2024-03-21T19:59:53.659228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 858
91.3%
0.01 50
 
5.3%
0.02 21
 
2.2%
0.03 4
 
0.4%
0.05 3
 
0.3%
0.07 1
 
0.1%
0.04 1
 
0.1%
0.11 1
 
0.1%
0.1 1
 
0.1%
ValueCountFrequency (%)
0 858
91.3%
0.01 50
 
5.3%
0.02 21
 
2.2%
0.03 4
 
0.4%
0.04 1
 
0.1%
0.05 3
 
0.3%
0.07 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
ValueCountFrequency (%)
0.11 1
 
0.1%
0.1 1
 
0.1%
0.07 1
 
0.1%
0.05 3
 
0.3%
0.04 1
 
0.1%
0.03 4
 
0.4%
0.02 21
 
2.2%
0.01 50
 
5.3%
0 858
91.3%

VeryActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct122
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.164894
Minimum0
Maximum210
Zeros409
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:53.814799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q332
95-th percentile93.05
Maximum210
Range210
Interquartile range (IQR)32

Descriptive statistics

Standard deviation32.844803
Coefficient of variation (CV)1.551853
Kurtosis5.7780701
Mean21.164894
Median Absolute Deviation (MAD)4
Skewness2.1761432
Sum19895
Variance1078.7811
MonotonicityNot monotonic
2024-03-21T19:59:54.002311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
8 15
 
1.6%
6 14
 
1.5%
11 14
 
1.5%
19 13
 
1.4%
5 13
 
1.4%
14 12
 
1.3%
Other values (112) 393
41.8%
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
4 10
 
1.1%
5 13
 
1.4%
6 14
 
1.5%
7 11
 
1.2%
8 15
 
1.6%
9 7
 
0.7%
ValueCountFrequency (%)
210 1
0.1%
207 1
0.1%
200 1
0.1%
194 1
0.1%
186 1
0.1%
184 1
0.1%
137 1
0.1%
132 1
0.1%
129 1
0.1%
125 2
0.2%

FairlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.564894
Minimum0
Maximum143
Zeros384
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:54.158552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q319
95-th percentile51
Maximum143
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.987404
Coefficient of variation (CV)1.4734656
Kurtosis7.9957314
Mean13.564894
Median Absolute Deviation (MAD)6
Skewness2.479492
Sum12751
Variance399.49632
MonotonicityNot monotonic
2024-03-21T19:59:54.331490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
40.9%
8 36
 
3.8%
6 23
 
2.4%
5 23
 
2.4%
16 22
 
2.3%
7 20
 
2.1%
10 19
 
2.0%
9 19
 
2.0%
13 18
 
1.9%
11 18
 
1.9%
Other values (71) 358
38.1%
ValueCountFrequency (%)
0 384
40.9%
1 10
 
1.1%
2 8
 
0.9%
3 9
 
1.0%
4 14
 
1.5%
5 23
 
2.4%
6 23
 
2.4%
7 20
 
2.1%
8 36
 
3.8%
9 19
 
2.0%
ValueCountFrequency (%)
143 1
 
0.1%
125 1
 
0.1%
122 1
 
0.1%
116 1
 
0.1%
115 1
 
0.1%
113 1
 
0.1%
98 1
 
0.1%
96 1
 
0.1%
95 5
0.5%
94 1
 
0.1%

LightlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.81277
Minimum0
Maximum518
Zeros84
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:54.503384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127
median199
Q3264
95-th percentile369.05
Maximum518
Range518
Interquartile range (IQR)137

Descriptive statistics

Standard deviation109.1747
Coefficient of variation (CV)0.56622132
Kurtosis-0.36011793
Mean192.81277
Median Absolute Deviation (MAD)69
Skewness-0.037929343
Sum181244
Variance11919.115
MonotonicityNot monotonic
2024-03-21T19:59:54.690879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
8.9%
206 12
 
1.3%
258 10
 
1.1%
195 9
 
1.0%
214 8
 
0.9%
139 7
 
0.7%
238 7
 
0.7%
141 7
 
0.7%
199 7
 
0.7%
227 7
 
0.7%
Other values (325) 782
83.2%
ValueCountFrequency (%)
0 84
8.9%
1 3
 
0.3%
2 4
 
0.4%
3 3
 
0.3%
4 1
 
0.1%
9 3
 
0.3%
10 2
 
0.2%
11 1
 
0.1%
12 2
 
0.2%
15 1
 
0.1%
ValueCountFrequency (%)
518 1
0.1%
513 1
0.1%
512 1
0.1%
487 1
0.1%
480 1
0.1%
475 1
0.1%
461 1
0.1%
458 1
0.1%
448 1
0.1%
439 1
0.1%

SedentaryMinutes
Real number (ℝ)

Distinct549
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.21064
Minimum0
Maximum1440
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:54.863862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile536.7
Q1729.75
median1057.5
Q31229.5
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)499.75

Descriptive statistics

Standard deviation301.26744
Coefficient of variation (CV)0.30393887
Kurtosis-0.66595003
Mean991.21064
Median Absolute Deviation (MAD)261
Skewness-0.29449809
Sum931738
Variance90762.068
MonotonicityNot monotonic
2024-03-21T19:59:55.035744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 79
 
8.4%
1182 7
 
0.7%
692 6
 
0.6%
1112 5
 
0.5%
1131 5
 
0.5%
1122 5
 
0.5%
1105 5
 
0.5%
709 5
 
0.5%
1119 5
 
0.5%
728 5
 
0.5%
Other values (539) 813
86.5%
ValueCountFrequency (%)
0 1
0.1%
2 1
0.1%
13 1
0.1%
48 1
0.1%
111 1
0.1%
125 1
0.1%
127 1
0.1%
218 1
0.1%
222 1
0.1%
241 1
0.1%
ValueCountFrequency (%)
1440 79
8.4%
1439 3
 
0.3%
1438 3
 
0.3%
1437 2
 
0.2%
1431 1
 
0.1%
1430 2
 
0.2%
1428 1
 
0.1%
1423 1
 
0.1%
1420 1
 
0.1%
1413 1
 
0.1%

Calories
Real number (ℝ)

Distinct734
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.6096
Minimum0
Maximum4900
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size7.5 KiB
2024-03-21T19:59:55.427384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1372.85
Q11828.5
median2134
Q32793.25
95-th percentile3654.25
Maximum4900
Range4900
Interquartile range (IQR)964.75

Descriptive statistics

Standard deviation718.16686
Coefficient of variation (CV)0.3117572
Kurtosis0.62502694
Mean2303.6096
Median Absolute Deviation (MAD)467
Skewness0.42245048
Sum2165393
Variance515763.64
MonotonicityNot monotonic
2024-03-21T19:59:55.614842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980 13
 
1.4%
2063 11
 
1.2%
1841 9
 
1.0%
1688 9
 
1.0%
1347 8
 
0.9%
2225 4
 
0.4%
1819 4
 
0.4%
2044 4
 
0.4%
1922 4
 
0.4%
0 4
 
0.4%
Other values (724) 870
92.6%
ValueCountFrequency (%)
0 4
0.4%
52 1
 
0.1%
57 1
 
0.1%
120 1
 
0.1%
257 1
 
0.1%
403 1
 
0.1%
665 1
 
0.1%
741 1
 
0.1%
928 1
 
0.1%
1002 1
 
0.1%
ValueCountFrequency (%)
4900 1
0.1%
4552 1
0.1%
4547 1
0.1%
4546 1
0.1%
4501 1
0.1%
4398 1
0.1%
4392 1
0.1%
4274 1
0.1%
4236 1
0.1%
4163 1
0.1%

Interactions

2024-03-21T19:59:47.609910image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:16.938888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.369756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.905585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.206683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.453914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.709887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.924896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:33.447418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.699963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.091521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.331976image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.713766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.035312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:47.786098image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:17.157648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.552366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.079989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.377667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.619298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.867328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.094986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:33.617457image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.880043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.271840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.513335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.888248image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.211508image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:47.960614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:17.321128image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.726198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.252498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.534779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.788630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.041564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.253020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:33.779169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.058062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.436844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.692273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.058100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.391448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.130196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:17.480638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.883280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.417867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.687545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.955823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.193392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.409915image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:33.940741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.218137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.574726image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.837211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.222638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.543706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.285891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:17.636908image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:20.067453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.578593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.854872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.101700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.354452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.585774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.094906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.367652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.748923image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.024916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.385240image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.709165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.447886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:17.881506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:20.374057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.727753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.006026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.267416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.492802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.753745image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.255481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.528941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:38.905542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.189842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.542203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:45.887786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.595684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.038709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:20.537584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:22.902541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.167264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.413478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.642495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:31.910436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.403810image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.694344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.060920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.336262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.700142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:46.042679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.774501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.225798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:20.713687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.065763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.316491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.582103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.815696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.076020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.553747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:36.883065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.223671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.524081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:43.868966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:46.214538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:48.945458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.394798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:20.860637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.230333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.485626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.735628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:29.965852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.241991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.727763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.055631image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.378753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.684681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.035259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:46.382849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:49.117681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.555690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.051775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.387304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.647404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:27.896969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.132977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.418274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:34.881208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.235991image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.540327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:41.858792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.205070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:46.554969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:49.275069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.704352image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.196715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.540612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.802575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.051686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.258903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.586287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.031936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.397008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.684280image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.018885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.353843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:46.714184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:49.449971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:18.867802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.387855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.703004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:25.965288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.221292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.448848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.759875image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.212768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.575861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:39.845803image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.189337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.526572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:47.094151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:49.608555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.041023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.557892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:23.854805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.122535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.380292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.604036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:32.909449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.363585image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.743190image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.001919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.374385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.684008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:47.254575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:49.793101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:19.218633image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:21.745307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:24.044505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:26.293907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:28.549518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:30.763525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:33.293400image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:35.537708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:37.916906image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:40.182312image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:42.550997image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:44.862727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-03-21T19:59:47.431147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-03-21T19:59:55.771090image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
IdTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesActivityDate
Id1.0000.1580.1990.1970.2100.2230.1110.030-0.1140.2510.125-0.084-0.0640.4290.000
TotalSteps0.1581.0000.9920.9920.1800.7700.7040.7150.0150.7490.6890.581-0.4280.5590.000
TotalDistance0.1990.9921.0001.0000.2030.7760.7010.7150.0130.7520.6850.559-0.4140.6170.000
TrackerDistance0.1970.9921.0001.0000.1930.7750.7010.7140.0110.7510.6860.558-0.4150.6170.000
LoggedActivitiesDistance0.2100.1800.2030.1931.0000.2260.1570.1390.0100.2650.1330.057-0.0870.2260.000
VeryActiveDistance0.2230.7700.7760.7750.2261.0000.7490.285-0.0640.9700.7430.158-0.2350.4970.000
ModeratelyActiveDistance0.1110.7040.7010.7010.1570.7491.0000.361-0.0960.7340.9800.244-0.3080.4030.016
LightActiveDistance0.0300.7150.7150.7140.1390.2850.3611.0000.1420.2850.3450.878-0.4660.4650.000
SedentaryActiveDistance-0.1140.0150.0130.0110.010-0.064-0.0960.1421.000-0.057-0.1030.1940.0960.0100.000
VeryActiveMinutes0.2510.7490.7520.7510.2650.9700.7340.285-0.0571.0000.7460.152-0.2410.5400.000
FairlyActiveMinutes0.1250.6890.6850.6860.1330.7430.9800.345-0.1030.7461.0000.232-0.3140.4350.051
LightlyActiveMinutes-0.0840.5810.5590.5580.0570.1580.2440.8780.1940.1520.2321.000-0.4800.2860.000
SedentaryMinutes-0.064-0.428-0.414-0.415-0.087-0.235-0.308-0.4660.096-0.241-0.314-0.4801.000-0.1520.097
Calories0.4290.5590.6170.6170.2260.4970.4030.4650.0100.5400.4350.286-0.1521.0000.119
ActivityDate0.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0510.0000.0970.1191.000

Missing values

2024-03-21T19:59:50.025097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-21T19:59:50.410542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
01.503960e+0912-04-2016131628.508.500.01.880.556.060.025133287281985
11.503960e+0913-04-2016107356.976.970.01.570.694.710.021192177761797
21.503960e+0914-04-2016104606.746.740.02.440.403.910.0301118112181776
31.503960e+0915-04-201697626.286.280.02.141.262.830.029342097261745
41.503960e+0916-04-2016126698.168.160.02.710.415.040.036102217731863
51.503960e+0917-04-201697056.486.480.03.190.782.510.038201645391728
61.503960e+0918-04-2016130198.598.590.03.250.644.710.0421623311491921
71.503960e+0919-04-2016155069.889.880.03.531.325.030.050312647752035
81.503960e+0920-04-2016105446.686.680.01.960.484.240.028122058181786
91.503960e+0921-04-201698196.346.340.01.340.354.650.01982118381775
IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCalories
9308.877689e+0903-05-2016108188.218.210.01.390.106.670.0119322911892817
9318.877689e+0904-05-20161819316.3016.300.010.420.315.530.0066821211543477
9328.877689e+0905-05-20161405510.6710.670.05.460.824.370.00671518811703052
9338.877689e+0906-05-20162172719.3419.340.012.790.296.160.00961723210954015
9348.877689e+0907-05-2016123328.138.130.00.080.966.990.001052827110364142
9358.877689e+0908-05-2016106868.118.110.01.080.206.800.0017424511742847
9368.877689e+0909-05-20162022618.2518.250.011.100.806.240.05731921711313710
9378.877689e+0910-05-2016107338.158.150.01.350.466.280.00181122411872832
9388.877689e+0911-05-20162142019.5619.560.013.220.415.890.00881221311273832
9398.877689e+0912-05-201680646.126.120.01.820.044.250.002311377701849